As artificial intelligence (AI) weaves its way into the fabric of daily life, its presence in education is sparking both innovation and controversy.
A recent article from Futurism highlights a growing trend: teachers are increasingly turning to AI tools to grade student work, driven by the widespread use of AI by students themselves.
With nearly 86% of university students using AI tools like ChatGPT, some educators are adopting a “fight fire with fire” approach, using AI to evaluate assignments. However, this practice raises critical questions about fairness, accuracy, and the future of education.
The integration of AI in grading is not a uniform practice but a spectrum of approaches. Some teachers, frustrated by students submitting AI-generated work, have embraced tools like Writable, which uses ChatGPT to provide feedback on essays.
Others, as noted in a Reddit post, take a pragmatic stance: “You are welcome to use AI. Just let me know. If you do, the AI will also grade you. You don’t write it, I don’t read it.”
Meanwhile, progressive educators are leveraging AI to personalize learning, such as tailoring math problems to individual students or requiring students to engage with AI feedback alongside human evaluations.
This shift is partly a response to the overwhelming workload faced by teachers. Tools like Brisk Teaching and MagicSchool promise to save educators hours by automating tasks like grading and lesson planning.
Brisk, for instance, allows teachers to provide targeted feedback directly within Google Docs, while MagicSchool boasts over 80 tools to streamline teaching tasks.
These platforms claim to alleviate burnout, with testimonials from teachers like Kelly Ann S., who credits Brisk with making her job “so much more manageable.”
Despite the allure of efficiency, AI grading systems are far from infallible. A study cited by Futurism found that large language models (LLMs) accurately graded student work only 33.5% of the time.
Even when equipped with human-designed rubrics, accuracy barely surpassed 50%. The issue lies in AI’s tendency to take shortcuts, misinterpreting student responses due to flawed logic.
For example, a student mentioning a “temperature increase” might lead an AI to assume they understand particle movement, a leap that human graders would scrutinize more carefully.
AI’s broader limitations compound this inaccuracy. Recent reports indicate that modern AI models hallucinate—generating false or misleading information—up to 79% of the time.
Such errors are particularly concerning in education, where precise feedback is crucial for student growth.
As Xiaoming Zhai, a researcher from the University of Georgia, noted, “While LLMs can adapt quickly to scoring tasks, they often bypass deeper logical reasoning expected in human grading.”
The use of AI for grading also ignites ethical debates. Students like Aidan, a high schooler quoted in Futurism, argue that it’s hypocritical for teachers to rely on AI while prohibiting students from doing the same.
This double standard can erode trust, suggesting that teachers prioritize convenience over fairness. Moreover, outsourcing grading to AI risks diminishing the human connection that drives effective teaching.
As the American Federation of Teachers emphasizes, “Real, consequential learning only happens when teachers and students collaborate in an atmosphere of mutual trust.”
Critics also warn of cognitive consequences. Overreliance on AI could undermine students’ critical thinking skills, a concern echoed by teachers like Gina Parnaby, who told Axios that AI can lead students to “outsource their thinking.”
This is particularly alarming for younger students, who lack the foundational knowledge to critically evaluate AI outputs.
Furthermore, the use of AI in administrative tasks, such as analyzing student data, raises privacy concerns, with tools like Securly Discern monitoring online behavior in ways that some students find intrusive.
